4288672
doi
10.5281/zenodo.4288672
oai:zenodo.org:4288672
Fontaine, Sylvain
Laboratoire de Physique Théorique et Modélisation, UMR-8089 CNRS, CY Cergy Paris Université
Hernández, Laura
Laboratoire de Physique Théorique et Modélisation, UMR-8089 CNRS, CY Cergy Paris Université
Cluster configurations of the Hegselmann-Krause model on network ensembles
Schawe, Hendrik
Laboratoire de Physique Théorique et Modélisation, UMR-8089 CNRS, CY Cergy Paris Université
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
<p>This is the raw data underlying the results of the preprint [arxiv:2102.10910](https://arxiv.org/abs/2102.10910).</p>
<p> </p>
<p>## Data</p>
<p>For each measured combination of the confidence and system size, there is one gzipped<br>
file. For different ensembles, we collected data in different ranges and quality.<br>
The paramters are:</p>
<p>* Number of samples `m` per parameter combination<br>
* Range `r` of confidences epsilon<br>
* Distances `d` between values of epsilon (basically the resolution of the data)<br>
* Largest size `N_max`</p>
<p>The single files follow a naming scheme of `n{N}_e{epsilon}.cluster.dat.gz`, where<br>
`{N}` signals the system size of the simulation and `{epsilon}` is the confidence<br>
value of the simulation (without a decimal point, i.e., `0050` corresponds to `epsilon = 0.050`).<br>
The sizes `N` are usually powers of two (or for the lattices, perfect squares close to powers of two).</p>
<p>We present the data for each ensemble in one archive.</p>
<p><br>
* Fully connected `full.tar`<br>
* `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 262144`<br>
* Barabasi Albert with a mean degree of 4 `BA4.tar`<br>
* `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 32768`<br>
* Barabasi Albert with a mean degree of 10 `BA10.tar`<br>
* `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`<br>
* Square lattice with first nearest neighbors `lat1.tar`<br>
* `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 16384`<br>
* Square lattice with second nearest neighbors `lat2.tar`<br>
* `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 16384`<br>
* Square lattice with third nearest neighbors `lat3.tar`<br>
* `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`<br>
* Square lattice with fourth nearest neighbors `lat4.tar`<br>
* `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`<br>
* Square lattice with third nearest neighbors and 1% rewired edges `lat3_ws.tar`<br>
* `m = 1000`, `r = [0.0, 0.3]`, `d = 0.001`, `N_max = 16384`<br>
* connected Erdos Renyi with mean degree of 10 `ER10.tar`<br>
* `m = 1000`, `r = [0.0, 0.3]`, `d = 0.002`, `N_max = 32768`</p>
<p> </p>
<p>## Data format</p>
<p>Each final state is encoded as three lines:</p>
<p>* The convergence time is a single integer with a line prefix '# sweeps: '<br>
* The positions of all clusters in opinion space with a line prefix '# ' (unsorted)<br>
* The number of agents in each of the clusters without a line prefix</p>
<p> </p>
<p>## Python example for reading the format</p>
<p>An example script, which visualizes the S vs eps graph for the largest size of the fully connected<br>
case, with a function to read this format is given in `example.py`.</p>
Zenodo
2020-11-24
info:eu-repo/semantics/other
4288671
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